This adds exactly the right kind of operational structure. You are formalizing
the "embodiment" of the state machine. By explicitly categorizing its
awareness into Environment, Inventory, Sensory Inputs, and Missions, you
transform it from a passive data store into an active, goal-oriented agent
that acknowledges its own limitations (being at the "mercy" of its sensors).
The three-tier mission structure (Macro/Sub/Micro) is a very clean way to
manage LLM attention spans without building a complex execution graph. It
gives the guiding intelligence (human or AI) the exact context needed to
decide the next move.
Here is how we integrate these concepts into the Design Document:
Additions to Section 2: Memory Topography
Update the topography to include the new categories:
 * The Environment (Location): The machine tracks its current context as a
single, routable object ID (e.g., DATA_ROOM#7bn3). It does not inherently know
what this room is; it relies on explicit /look commands or sensory add-ons to
resolve this ID. If unknown, the environment defaults to VOID.
 * Mission State (The Motivational Triad): The machine maintains three
distinct strings representing its current objectives, acting as the primary
prompt context when requesting guidance:
   * Macro (Global): The overarching goal (e.g., "Negotiate the server
contract with CUSTOMER#Zv82q9").
   * Sub (Current Branch): The immediate strategic focus (e.g., "Verify the
pricing terms in DOCUMENT#p9L2").
   * Micro (Tactical/Interrupt): The immediate next action, often handling
interruptions or retries (e.g., "Ask for clarification on the rejected payload
from the environment").
Additions to Section 4: Execution Loop & REPL (Commands)
Expand the core commands to support the new categories:
 * /look -> Queries the currently known Environment ID (or requests the
sensory add-on to resolve the current context) and prints the last known data
about the room/location.
 * /inventory -> Forces a refresh/printout of the currently held objects
(e.g., INSTRUCTIONS#1W2E). This helps the operator/AI realign with what tools
or data are currently available to the machine.
 * /mission [macro|sub|micro] [text] -> Sets or rewrites the respective
mission focus.
 * /tick -> A command (which can be called manually or scheduled periodically)
that bundles the Current Environment, Inventory, and Mission State, and sends
them to the plugged-in decision engine (Human or AI) requesting the next
command(s).
New Section 7: Sensory Add-ons & The Perception Engine
7. Sensory Add-ons & The Perception Engine
The state machine operates under the philosophical assumption that it is
"blind" and entirely at the mercy of external inputs. It does not parse raw
data feeds itself.
 * The Sensor Interface: The architecture supports plugging in arbitrary
"Sensory Add-ons" (e.g., an LLM monitoring a Discord channel, a script
scraping an API, or a human typing in the REPL).
 * Perception via Mailbox: These sensors do not write directly to the state
machine's memory. They translate raw data into the AST-MD standard and drop it
into the state machine's mailbox.
 * Interpreting the Environment: If a sensor detects a context shift, it sends
an event like UPDATE ENVIRONMENT : current = DATA_ROOM#7bn3. The state machine
updates its internal pointer, relying entirely on the sensor's interpretation
of reality.
 * The /tick Cycle: When operating autonomously, the state machine fires a
/tick event. This gathers its current limitations (what it knows it doesn't
know), its Inventory, and its 3-tier Mission State, and asks the primary
sensory/decision AI: "Given my limited perception of DATA_ROOM#7bn3 and my
micro-focus of 'retry failed command', what should I execute next?"